An Investigation of Driver Distraction Near the Tipping
Point of Traffic Flow Stability
Joel M. Cooper, Ivana Vladisavljevic, Nathan Medeiros-Ward, Peter T. Martin,
and David L. Strayer, University of Utah, Salt Lake City, Utah
Objective: The purpose of this study was to explore the interrelationship between driver
distraction and characteristics of driver behavior associated with reduced highway traffic
efficiency. Background: Research on the three-phase traffic theory and on behavioral
driving suggests that a number of characteristics associated with efficient traffic flow may
be affected by driver distraction. Previous studies have been limited, however, by the fact
that researchers typically do not allow participants to change lanes, nor do they account
for the impact of varying traffic states on driving performance. Methods: Participants
drove in three simulated environments with differing traffic congestion while both using
and not using a cell phone. Instructed only to obey the speed limit, participants were
allowed to vary driving behaviors, such as those involving forward following distance,
speed, and lane-changing frequency. Results: Both driver distraction and traffic congestion were found to significantly affect lane change frequency, mean speed, and the likelihood of remaining behind a slower-moving lead vehicle. Conclusions: This research
suggests that the behavioral profile of “cell phone drivers,” which is often described
as compensatory, may have far-reaching and unexpected consequences for traffic efficiency. Application: By considering the dynamic interplay between characteristics of
traffic flow and driver behavior, this research may inform both public policy regarding
in-vehicle cell phone use and future investigations of driving behavior.
INTRODUCTION
In the past few decades, traffic delays caused
by highway congestion have become a serious problem. As a result, traffic researchers
have turned toward identifying and eliminating
traffic inefficiencies. One such cause of traffic
inefficiency may be driver distraction. Indeed,
driver characteristics thought to contribute to
efficient traffic flow, such as rapid reaction
time, consistent speed, appropriate following distance, and anticipatory visual scanning
(Brackstone, McDonald, & Wu,1998; Davis,
2004; Huang, 2002; Kerner, 2004; Knospe,
Santen, Schadschneider, & Schreckenberg,
1999, 2002; Treiber, Kesting, & Helbing,
2006a, 2006b), may all show impairment when
a driver’s attention is diverted toward a secondary task (Beede & Kass, 2006; Horrey
& Wickens, 2006; Recarte, & Nunes, 2000;
Strayer & Drews, 2004; Törnros & Bolling,
2006). These studies are limited, however, by
the fact that the researchers typically did not
allow participants to change lanes, and they
did not account for the impact of varying traffic congestion on driving performance. Thus,
it is not known how distraction affects unconstrained driving performance as traffic density
increases toward a congested state.
According to the three-phase traffic theory
(Kerner, 2004), the relationship between drivers
and traffic can be characterized as a complex
system with emergent properties, bidirectional
causality, and mutual constraints. From this perspective, the various patterns of traffic flow are
determined by driver behaviors (Huang, 2002),
and driver behaviors are, in turn, affected by
patterns in traffic flow (Treiber et al., 2006b).
This research provides a bridge between these
levels of organization by investigating the effect
Address correspondence to Joel M. Cooper, Department of Psychology, 380 S., 1520 E., Rm. 502, University of Utah,
Salt Lake City, UT 84112; joel.cooper@psych.utah.edu. HUMAN FACTORS, Vol. 51, No. 2, April 2009, pp. 261-268.
DOI: 10.1177/0018720809337503. Copyright © 2009, Human Factors and Ergonomics Society.
262
April 2009 - Human Factors
Figure 1. A representation of the three-phase traffic theory, cross-plotting flow and density. Shaded areas represent commonly observed flow–density traffic relationships for both free-flow and synchronized-flow traffic
phases. Adapted from “Basis of Three Phase Traffic Theory” by B. Kerner, 2004, in The Physics of Traffic:
Empirical Freeway Pattern Features, Engineering Applications, and Theory, p. 96, Figure 4.5. Copyright
2004 by Springer-Verlag Berlin Heidelberg. Adapted with kind permission of Springer Science+Business
Media.
of various levels of freely flowing traffic on
driving behavior while considering the broader
implications for traffic that could arise from
these driving behaviors.
Driving in Traffic
On a macroscopic level, traffic movement
is generally characterized by three interrelated
indices of efficiency: flow (i.e., the number of
vehicles per hour per lane), density (i.e., the
number of cars per mile per lane), and speed
(i.e., the average vehicle speed). A generalized
representation of the interrelationship between
flow and density is shown in Figure 1. This
figure illustrates the important finding that
some levels of identical highway demand are
bi-stable, meaning that the same rate of vehicle
flow or density can generate two possible flow
patterns. This is seen in the two darkly hatched
areas, which occur at similar vehicle densities
but at different flows. In one case, higher flow
rates result from higher average traffic speeds,
whereas in the other case, average speeds and
flow are reduced. Kerner (2004) suggested that
this sudden loss of traffic flow is analogous to
a phase transition in thermodynamics, in which
the general patterning of highway traffic is rapidly reorganized from one state (e.g., free flow)
to another (e.g., synchronized flow).
In multilane roadways, free-flow traffic is
characterized by high, asynchronous speeds in
each lane; modest forward following distances;
and frequent lane changes (Huang, 2002; Kerner,
2004; Treiber et al., 2006a). These characteristics create flow stability that is robust to a wide
variety of traffic perturbations. However, as
highway demand increases toward the capacity
ceiling, vehicles begin to interact, the stability
of free flow is reduced, and even slight variations in driving behaviors could disproportionately affect traffic by causing a premature traffic
phase transition (Nagel & Paczuski, 1995).
This transition to synchronized flow results in
reductions in lane change opportunities, frequent speed fluctuations, reduced speeds, and
increased travel time.
Distracted Driving In Traffic
Although the precise interrelationship between
driver characteristics and the onset of traffic
congestion is still debated, a number of factors
that contribute to inefficient traffic flow have
been identified. In general, highway efficiency
is reduced by vehicles that proceed more slowly
than surrounding traffic, display greater speed
variability, cut off other drivers during a lane
change, respond more slowly to sudden-onset
events, and exhibit greater-than-necessary time
headways (Brackstone et al.,1998; Davis, 2004;
Huang, 2002; Kerner, 2004; Knospe et al., 1999,
2002; Treiber et al., 2006a, 2006b).
Although previous research on distracted
driving has not investigated the effect of varying
traffic characteristics on these measures, a number of studies have reported that distracted drivers exhibit general reductions in average speed
(Burns, Parkes, Burton, Smith, & Burch, 2002;
Haigney, Taylor, & Westerman, 2000), increased
speed variability (Green, Hoekstra, & Williams,
1993; Reed & Green, 1999), delayed response
time (Alm & Nilsson, 1995; Strayer & Johnston,
2001), and increases in vehicle following distance (Greenberg et al., 2003; Strayer & Drews,
2004; Strayer, Drews, & Crouch, 2006). In addition, Beede and Kass (2006) found that drivers
in a city environment made fewer lane changes
when distracted by a secondary task. In this way,
the behavior of distracted drivers is consistent
with the general characteristics of traffic in the
synchronized-flow phase, suggesting that driver
distraction may reduce highway efficiency by
hastening the onset, or increasing the duration, of
synchronized traffic flow.
The Current Research
The aim of the current research was to
explore the influence of a secondary task on
unconstrained driving behavior in three levels of
freely flowing traffic. Although there are many
forms of driver distraction, a cell phone conversation was used because of the prevalence of
in-vehicle cell phone use. This research was
guided by the hypothesis that in freely flowing
traffic, in-vehicle cell phone conversations would
elicit driving behaviors consistent with broad
traffic characteristics in the synchronized-flow
phase. To explore this hypothesis, we allowed
drivers to freely change lanes and proceed at
their own pace, restricted only by the speed
limit and behavior of surrounding vehicles.
263
Figure 2. A typical participant conversing on a
hands-free cell phone in a three-lane section of the
high-flow driving scenario.
Consistent with Beede and Kass (2006),
who studied lane-changing behavior in a city
environment, we reasoned that concurrent cell
phone conversation would cause a reduction
in lane changes. In addition, we predicted
that lane-change frequency would indicate an
inverse U–shaped function as density increased
(Huang, 2002). With respect to the quality of
lane-changing maneuvers, we expected that
the widely studied perceptual deficits associated with in-vehicle cell phone conversation (McCarley et al., 2004; Strayer, Cooper,
& Drews, 2004) would result in poorer lane
changes, possibly leading to increased instances
of cutting off other vehicles. Additionally, we
expected that drivers on a cell phone would
increase their following distance relative to
leading vehicles. Finally, on the basis of previous findings, we expected that drivers on a cell
phone would reduce their speed across all levels
of traffic congestion.
METHOD
Participants
Thirty-six undergraduates from a local university participated in the research (mean age
21.5 years). All had normal or corrected-tonormal visual acuity and a valid driver’s license.
Stimuli and Apparatus
A PatrolSim (manufactured by L3 communications) high-fidelity fixed-base driving simulator, illustrated in Figure 2, was used in the
study. The simulator incorporates proprietary
vehicle dynamics with traffic scenario and road
264
surface software to provide realistic scenes and
traffic conditions. The dashboard instrumentation, steering wheel, gas pedal, and brake pedal
were taken from a Ford Crown Victoria® sedan
with an automatic transmission.
The highway database simulated a 38.6-km
(24-mile) multilane roadway with on- and offramps, overpasses, and two- and three-lane traffic
in each direction. Three unique driving scenarios, 14.8 km (9.2 miles) in length, were created
from this database. Each 14.8-km (9.2-mile)
scenario began with a 6.3-km (3.9-mile) stretch
of two-lane traffic in each direction, followed
by 8.5 km (5.3 miles) of three lanes of traffic in
each direction. A large, grassy median separated
the two directions of traffic.
The traffic flow, speed, and model of simulated vehicles differed in each scenario. Traffic in
the low-, medium-, and high-flow scenarios was
calibrated to 1,450, 1,850, and 2,250 vehicles
per hour per lane, respectively, and consisted of
approximately 10% heavy vehicles (semi trucks,
dump trucks, etc.). Traffic speed in the low-flow
driving condition fluctuated between 96.5 km/h
and 112.7 km/h (between 60 mph and 70 mph)
with a mean speed of 103 km/h (64 mph). Traffic
speed for the medium-flow condition fluctuated
between 72.4 km/h and 88.5 km/h (between
45 mph and 55 mph) with a mean speed of
80.5 km/h (50 mph).Traffic speed in the highflow condition fluctuated between 64.4 km/h and
80.5 km/h (between 40 mph and 50 mph) with a
mean speed of 69.2 km/h (43 mph). These parameters were targeted to reflect actual traffic conditions recorded by traffic-monitoring stations on
a major interstate in the western United States.
Procedure
Upon arrival, participants completed a questionnaire assessing their interest in potential
topics of cell phone conversation. Participants
were then familiarized with the driving simulator using a standardized 20-min adaptation
sequence, after which the data collection began.
Participants drove each of the three scenarios
in both single- and dual-task conditions, resulting in six driving performance observations, a
3 (traffic flow) × 2 (distraction) repeated-measures design. The order of scenario presentation
and conversation condition was counterbalanced. Participants were instructed to drive as
April 2009 - Human Factors
they normally would during a typical highway
situation. They were informed that they were
free to change lanes and were reminded to use
their turn signals.
The dual-task condition involved naturalistic
conversation with a confederate via cell phone.
Once initiated, conversation was allowed to
progress and develop naturally. In the cases in
which natural conversation was not sufficient to
maintain a constant exchange, the confederate
generated additional dialogue based on the participant’s questionnaire. To avoid any possible
interference from manual components of cell
phone use, participants used a hands-free cell
phone that was positioned and adjusted before
driving began. Additionally, the call was initiated
before participants began the dual-task scenarios
and lasted until scenario completion.
Dependent Measures
The dependent measures that were collected
and analyzed for this research included the
following: Lane change frequency was defined
as the number of instances participants exited
their lane and fully entered an adjacent lane.
Lag distance was defined as the distance in
meters between the center of the participant’s
vehicle and the center of the nearest following
vehicle located in the selected lane during a lane
change. Lag distance was assessed once participants fully entered an adjacent lane and only
for vehicles within the following/not-following
threshold of 60 m. Following ratio was defined
as the percentage of each drive that a participant’s vehicle was less than 60 m from a leading vehicle in the same lane. Forward following
distance was defined as the distance in meters
from the center of the participant’s vehicle to
the center of the nearest lead vehicle in the same
lane, measured only when the participant’s vehicle was within 60 m of a lead vehicle. Driving
speed was defined as the mean of a participant’s
speed in miles per hour.
RESULTS
With the exception of lag distance, each of
the dependent measures was analyzed using a 3
× 2 repeated-measures general linear model. All
p values for the distraction comparisons were
divided by 2 to account for the directionality of
our hypotheses. Multiple planned comparisons
Distracted Driving In Traffic
265
TABLE 1: Driving Performance Variables for Single- and Dual-Task Conditions by Traffic Flow
Dependent Measure
Lane change frequency
Single task
Dual task
Following ratio
Single task
Dual task
Forward following distance (m)
Single task
Dual task
Driving speed (km/h)
Single task
Dual task
Low Flow
Medium Flow
High Flow
M
SD
M
SD
M
SD
4.50
4.80
3.30
3.50
7.90
6.30
3.70
3.50
7.50
6.10
4.30
2.70
0.20
0.26
0.13
0.14
0.37
0.42
0.14
0.12
0.46
0.52
0.13
0.11
35.60
35.50
8.94
7.31
30.40
30.80
4.51
5.20
27.70
27.70
3.97
3.93
110.60
110.90
4.00
6.40
95.80
92.20
8.00
8.00
79.80
77.10
8.90
6.90
Figure 3. A grouped frequency chart of lag gaps for lane change maneuvers in the low-, medium-, and highflow highway driving conditions.
were then used to assess the effects of distraction at each level of traffic flow. The results of
these comparisons are in Table 1.
Preliminary analysis of lag distance indicated that the majority of drivers often changed
lanes with greater than 60 m between them and
a following vehicle, and a minority frequently
changed lanes with less than 60 m of lag distance.
Because the average lag distance did not capture
this meaningful variation, we elected instead to
include in a chi-square analysis each lane change
leaving less than 60 m of lag distance.
Lane Change Frequency
An overall analysis of lane-change frequency
found that it was significantly reduced by the
distraction condition, F(1, 35) = 3.78, p < .05,
and traffic flow, F(2, 70) = 17.96, p < .001;
however, the interaction between flow and distraction was not significant, F(3, 105) = 2.96,
p > .05. Additional analyses indicated that
when drivers conversed on the phone, they
made fewer lane changes in the medium-flow,
t(35) = 2.91, p < .01, and high-flow conditions,
266
t(35) = 1.76, p < .05, whereas the distraction
condition had no effect on lane change frequency
in the low-flow condition, t(35) = .34, p > .05.
Lag Distance
Drivers changed lanes with less than 60 m of
lag distance 299 times. Of those, 158 occurred
in the single-task condition and 141 in the dualtask condition. Figure 3 indicates that lane
changes leaving less than 40 m of lag distance
were more common for drivers in the dual-task
condition, and lane changes leaving between
40 m and 60 m of lag distance were more common for drivers in the single-task condition,
χ2(1) = 5.37, p < .05. Overall, when conversing
on a cell phone, drivers were 11% more likely to
change lanes with less than 40 m of lag space.
Following Ratio
Compared with the single-task condition,
drivers on cell phones spent more time following within 60 m of a lead vehicle, F(1, 35) =
11.43, p < .001. Post hoc comparisons revealed
that these differences were significant in the
low-flow, t(35) = 2.76, p < .01, medium-flow,
t(35) = 1.73, p < .05, and high-flow, t(35) =
1.96, p < .05, conditions. Not surprisingly,
drivers spent significantly more time following a leading vehicle as traffic flow increased,
F(2, 70) = 140.46, p < .001; however, the
interac­tion between traffic flow and distraction
was not significant, F < 1.
Forward Following Distance
As expected, forward following distance
decreased as traffic flow increased, F(2, 70) =
42.22, p < .001. However, contrary to our
expectations, forward following distance was
not affected by distraction, F < 1, and likewise,
the interaction between traffic flow and distraction was not significant, F < 1.
Driving Speed
Driving speed was significantly reduced
as traffic flow increased and average speeds
decreased, F(2, 70) = 384.4, p < .001. In addition, mean speed for drivers in the dual-task
condition was significantly lower than for drivers in the single-task condition, F(1, 35) = 4.73,
p < .05. This difference appeared to be independent of traffic flow, as the interaction between
April 2009 - Human Factors
traffic flow and distraction was not significant,
F(3, 105) = 2.36, p > .05. Post hoc comparisons
indicated that the driving speeds under singleand dual-task conditions were significantly different for the medium-flow, t(35) = 1.94, p <
.05, and high-flow conditions, t(35) = 1.88, p <
.05, whereas no effect of distraction on speed
was observed in the low-flow driving condition,
t(35) = –.49, p > .05.
Additional analyses of speed indicated that
mean speed for drivers on cell phones did not
differ when drivers were either following,
F(1, 35) = 0.04, p > .05, or not following a lead
vehicle, F(1, 35) = 0.56, p > .05. This finding
suggests that drivers in the single-task condi­
tion maintained slightly higher speeds than did
drivers conversing on cell phones by selecting
lanes with less congestion.
DISCUSSION
The purpose of this study was to explore the
interrelationship between driver distraction and
characteristics of driver behavior associated
with reduced traffic flow. The speed and spacing of scenario vehicles were designed to probe
driving behavior in a range of traffic situations
representative of the free-flow phase in Kerner’s
(2004) three-phase traffic theory. This research
was guided by the overarching hypothesis that
in freely flowing traffic, distraction from a secondary task would elicit driving behaviors that
are consistent with the reduced traffic efficiency
of synchronized flow.
On the basis of previous investigations, we
expected that drivers on cell phones would
exhibit the following: a reduction in lane changes,
an inverse U–shaped distribution of lane change
frequency across the three flow conditions,
poorer lane changes, an increase in forward
following distance, and a reduction in mean
driving speed. Results were largely consistent
with these original hypotheses; however, two
findings were unexpected.
First, with the exception of following ratio,
driving in the low-flow condition did not appear
to be affected by concurrent cell phone conversation. We attribute this finding to the fact that
average traffic speeds in the low-flow scenario
were very high and near the posted scenario
speed limit. This may have set a performance
ceiling that was easily attainable irrespective of
Distracted Driving In Traffic
secondary task demand. In some respects, this
finding is consistent with findings by Strayer,
Drews, and Johnston (2003), who reported that
the addition of surrounding traffic was needed
to observe an impact of cell phone conversations on driving.
Second, drivers on cell phones did not maintain greater forward following distances. We
attribute this finding to the stability of traffic
speeds and the ability of drivers to freely vary
their lane position. In other research, forward
following distance is often recorded using a
car-following paradigm in which drivers are
instructed to remain behind a lead vehicle without passing (Strayer et al., 2006). Participants
were then commonly exposed to frequent and
unpredictable braking events designed to simulate stop-and-go traffic and to collect brake reaction time. The speed variability introduced by
frequent braking events may differentially affect
drivers in single- and dual-task conditions. This
conjecture merits further exploration, as the
general tendency for drivers to increase forward
following distance in the face of increased lead
vehicle variability has been suggested as a primary determinant of a phase transition from free
to synchronized flow (Treiber et al., 2006a).
Notably, research by Rosenbloom (2006)
that did not require drivers to follow a lead
vehicle or respond to frequent and unpredictable braking events found that cell phone conversation actually decreased forward following
distances. These findings suggest that when one
removes the constraint to follow a lead vehicle, the increases in forward following distance
often reported for distracted drivers may be
situational and not characteristic of distracted
driving in general.
In the medium- and high-flow conditions,
drivers on the cell phone made fewer lane
changes than did drivers in the single-task condition. On one hand, this may be construed as
a benefit to safety, as changing lanes has been
cited as one of the factors most frequently
involved in highway accidents (Jeffcoate,
Skelton, & Smeed, 1970; Pande & Abdel-Aty,
2006). On the other hand, drivers’ ability to
change lanes counteracts the tendency of the
slowest driver to dominate flow (Chowdhury,
Wolf, & Schreckenberg, 1997; Sparman, 1979).
267
Nonetheless, the analysis of lag distance suggests that distraction led to less safe lane
changes. Thus, any benefit to safety that may
have been gained by making fewer lane changes
in the dual-task condition appears to have been
offset by the quality of those lane changes.
CONCLUSION
Single- and dual-task performance differences
observed in this investigation provide support
for the hypothesis that driver distraction leads
to driving behaviors associated with congested
traffic. In the medium- and high-flow scenarios, a number of driving measures were sensitive to in-vehicle cell phone conversation even
though drivers were free to change lanes and to
proceed at their own pace. These findings suggest that near the tipping point of traffic stability, the effect of driver distraction may not be
isolated to the distracted driver but could have
far-reaching and unexpected consequences for
traffic flow. Given that an estimated 10% of
drivers are conversing on a cell phone during a
typical daylight moment (Glassbrenner, 2005),
the overall impact of cell phone use on traffic
flow could be substantial.
ACKNOWLEDGMENTS
A preliminary version of these data was
presented at the 87th annual meeting of the
Transportation Research Board, January 13-17,
2008, Washington, D.C.
REFERENCES
Alm, H., & Nilsson, L. (1995). The effects of a mobile telephone
task on driver behaviour in a car following situation. Accident
Analysis & Prevention, 27, 707–715.
Beede, K. E. & Kass, S. J. (2006). Engrossed in conversation:
The impact of cell phones on simulated driving performance.
Accident Analysis & Prevention, 38, 415–421.
Brackstone, M., McDonald, M., & Wu, J. (1998). Lane changing
on the motorway: Factors affecting its occurrence, and their
implications. In Proceedings of the 9th International
Conference on Road Transport Information and Control
(pp. 160–164). New York: Institute of Electrical and Electro­
nics Engineers.
Burns, P. C., Parkes, A., Burton, S., Smith, R. K., & Burch, D.
(2002). How dangerous is driving with a mobile phone?
Benchmarking the impairment to alcohol (TRL Report 547).
Berkshire, UK: Transportation Research Laboratory.
Chowdhury, D., Wolf, D. E., & Schreckenberg, M. (1997).
Particle hopping models for two-lane traffic with two kinds
of vehicles: Effects of lane-changing rules. Physica A, 235,
417–439.
268
Davis, L. C. (2004). Multilane simulations of traffic phases.
Physical Review E, 69, 026124.1-026124.5.
Glassbrenner, D. (2005). Driver cell phone use in 2005: Overall
results. Traffic safety facts research note (Report No. DOT-HS809-967). Washington, DC: National Highway Traffic Safety
Administration.
Green, P., Hoekstra, E., & Williams, M. (1993). Further on-theroad tests of driver interfaces: Examination of a route guidance system and car phone (Report No. UMTRI-93-35).
Ann Arbor: University of Michigan Transportation Research
Institute.
Greenberg, J., Tijerina, L., Curry, R., Artz, B., Cathey, L., Kochhar,
D., et al. (2003). Evaluation of driver distraction using an event
detection paradigm. Journal of the Transportation Research
Board, 1843, 1–9.
Haigney, D. E., Taylor, R. G., & Westerman, S. J. (2000).
Concurrent mobile (cellular) phone use and driving performance: Task demand characteristics and compensatory processes. Transportation Research Part F, 3, 113–121.
Horrey, W. J., & Wickens, C. D. (2006). Examining the impact of
cell phone conversations on driving using meta-analytic techniques. Human Factors, 48, 196–205.
Huang, D. (2002). Lane-changing behavior on highways. Physics
Review E, 66, 026124.1-026124.5.
Jeffcoate, G. O., Skelton, N., & Smeed, R. J. (1970). Analyses
of national accident statistics of overtaking road accidents.
London: University College, Traffic Studies Group.
Kerner, B. (2004). The physics of traffic: Empirical freeway pattern features, engineering applications, and theory. Berlin:
Springer-Verlag.
Knospe, W., Santen, L., Schadschneider, A., & Schreckenberg, M.
(1999). Disorder effects in cellular automata for two-lane traffic. Physica A, 265, 614–633.
Knospe, W., Santen, L., Schadschneider, A., & Schreckenberg, M.
(2002). Single-vehicle data of highway traffic: Microscopic
description of traffic phases. Physical Review E, 65, 056133.­1056133.16.
McCarley, J. S., Vais, M. J., Pringle, H., Kamer, A. F., Irwin, D. E.,
& Strayer, D. L. (2004). Conversation disrupts change
detection in complex traffic scenes. Human Factors, 46,
424–436.
Nagel, K., & Paczuski, M. (1995). Emergent traffic jams. Physical
Review E, 51, 2909-2918.
Pande, A., & Abdel-Aty, M. (2006). Assessment of freeway traffic
parameters leading to lane-change related collisions. Accident
Analysis & Prevention, 38, 936–948.
Recarte, M. A., & Nunes, L. M. (2000). Effects of verbal and spatial-imagery tasks on eye fixations while driving. Journal of
Experimental Psychology: Applied, 6, 31–43.
Reed, M. P., & Green, P. A. (1999). Comparison of driving performance on-road and in low-cost simulator using a concurrent
telephone dialing task. Ergonomics, 42, 1015–1037.
Rosenbloom, T. (2006). Driving performance while using cell
phones: An observational study. Journal of Safety Research,
37, 207–212.
Sparman, U. (1979, June). The importance of lane changing on
motorways. Traffic Engineering and Control, pp. 320–323.
April 2009 - Human Factors
Strayer, D. L., Cooper, J. M., & Drews, F. A. (2004). What do drivers fail to see when conversing on a cell phone? In Proceedings
of the Human Factors and Ergonomics Society 48th Annual
Meeting (pp. 2213–2217). Santa Monica, CA: Human Factors
and Ergonomics Society.
Strayer, D. L., & Drews, F. A. (2004). Profiles in driver distraction: Effects of cell phone conversations on younger and older
drivers. Human Factors, 46, 640–649.
Strayer, D. L., Drews, F. A., & Crouch, D. J. (2006). A comparison
of the cell phone driver and the drunk driver. Human Factors,
48, 381–391.
Strayer, D. L., Drews, F. A., & Johnston, W. A. (2003). Cell phone
induced failures of visual attention during simulated driving.
Journal of Experimental Psychology: Applied, 9, 23–23.
Strayer, D. L., & Johnston, W. A. (2001). Driven to distraction:
Dual-task studies of simulated driving and conversing on a cellular phone. Psychological Science, 12, 462-466.
Törnros, J., & Bolling, A. (2006). Mobile phone use: Effects of
conversation on mental workload and driving speed in rural
and urban environments. Transportation Research Part F:
Traffic Psychology and Behavior, 9, 298–306.
Treiber, M., Kesting, A., & Helbing. D. (2006a). Delays, inaccuracies and anticipation in microscopic traffic models. Physica
A, 360, 71–88.
Treiber, M., Kesting, A., & Helbing, D. (2006b). Understanding
widely scattered traffic flows, the capacity drop, and platoons
as effects of variance-driven time gaps. Physics Review E, 74,
016123.1-016123.10.
Joel M. Cooper is a graduate student in the Depart­
ment of Psychology at the University of Utah in Salt
Lake City. He received his MS from the University
of Utah in Salt Lake City in 2007.
Ivana Vladisavljevic is a graduate student in civil
and environmental engineering at the University of
Utah in Salt Lake City. She received her MS from the
University of Utah in 2006.
Nathan Medeiros-Ward is a graduate student in
the Department of Psychology at the University of
Utah in Salt Lake City.
Peter Martin is an associate professor of civil and
environmental engineering at the University of Utah
in Salt Lake City. He received his PhD from the
University of Nottingham in England in 1992.
David Strayer is a professor of psychology at the
University of Utah in Salt Lake City. He received
his PhD from the University of Illinois in UrbanaChampaign in 1989.
Date received: July 17, 2008
Date accepted: March 23, 2009